Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It discloses that the tool persists predictions, returns a call_id for later feedback, and uses a resolved-outcomes ledger. It does not mention destructive actions or rate limits, but the described behavior is consistent with a safe calibration tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loaded with the primary purpose, and every sentence adds value. There is no redundancy or fluff, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description thoroughly explains the return values (calibrated_confidence, similar cases, etc.), the persistence and feedback mechanism, and the backing ledger. For a tool with 4 parameters (1 required), this is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description adds context that stakes are used for kelly_stake and domain for filtering similar cases, but it does not significantly elaborate beyond the schema. This is adequate but not exceptional.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'calibrate' and the resource 'prediction's confidence against historical outcomes'. It details the return values and the backing ledger, and it implicitly distinguishes itself from sibling 'batch_calibrate' by focusing on a single prediction and the feedback loop.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a usage instruction for closing the loop with feedback API, but it does not explicitly guide when to use this tool vs. siblings like 'batch_calibrate' or 'get_domain_accuracy'. The context is clear but lacks explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.